Python Typing Patterns

Modern type hints for safe, documented Python code.

Basic Annotations

# Variables
name: str = "Alice"
count: int = 42
items: list[str] = ["a", "b"]
mapping: dict[str, int] = {"key": 1}

# Function signatures
def greet(name: str, times: int = 1) -> str:
    return f"Hello, {name}!" * times

# None handling
def find(id: int) -> str | None:
    return db.get(id)  # May return None

Collections

from collections.abc import Sequence, Mapping, Iterable

# Use collection ABCs for flexibility
def process(items: Sequence[str]) -> list[str]:
    """Accepts list, tuple, or any sequence."""
    return [item.upper() for item in items]

def lookup(data: Mapping[str, int], key: str) -> int:
    """Accepts dict or any mapping."""
    return data.get(key, 0)

# Nested types
Matrix = list[list[float]]
Config = dict[str, str | int | bool]

Optional and Union

# Modern syntax (3.10+)
def find(id: int) -> User | None:
    pass

def parse(value: str | int | float) -> str:
    pass

# With default None
def fetch(url: str, timeout: float | None = None) -> bytes:
    pass

TypedDict

from typing import TypedDict, Required, NotRequired

class UserDict(TypedDict):
    id: int
    name: str
    email: str | None

class ConfigDict(TypedDict, total=False):  # All optional
    debug: bool
    log_level: str

class APIResponse(TypedDict):
    data: Required[list[dict]]
    error: NotRequired[str]

def process_user(user: UserDict) -> str:
    return user["name"]  # Type-safe key access

Callable

from collections.abc import Callable

# Function type
Handler = Callable[[str, int], bool]

def register(callback: Callable[[str], None]) -> None:
    pass

# With keyword args (use Protocol instead)
from typing import Protocol

class Processor(Protocol):
    def __call__(self, data: str, *, verbose: bool = False) -> int:
        ...

Generics

from typing import TypeVar

T = TypeVar("T")

def first(items: list[T]) -> T | None:
    return items[0] if items else None

# Bounded TypeVar
from typing import SupportsFloat

N = TypeVar("N", bound=SupportsFloat)

def average(values: list[N]) -> float:
    return sum(float(v) for v in values) / len(values)

Protocol (Structural Typing)

from typing import Protocol

class Readable(Protocol):
    def read(self, n: int = -1) -> bytes:
        ...

def load(source: Readable) -> dict:
    """Accepts any object with read() method."""
    data = source.read()
    return json.loads(data)

# Works with file, BytesIO, custom classes
load(open("data.json", "rb"))
load(io.BytesIO(b"{}"))

Type Guards

from typing import TypeGuard

def is_string_list(val: list[object]) -> TypeGuard[list[str]]:
    return all(isinstance(x, str) for x in val)

def process(items: list[object]) -> None:
    if is_string_list(items):
        # items is now list[str]
        print(", ".join(items))

Literal and Final

from typing import Literal, Final

Mode = Literal["read", "write", "append"]

def open_file(path: str, mode: Mode) -> None:
    pass

# Constants
MAX_SIZE: Final = 1024
API_VERSION: Final[str] = "v2"

Quick Reference

Type	Use Case
X \| None	Optional value
list[T]	Homogeneous list
dict[K, V]	Dictionary
Callable[[Args], Ret]	Function type
TypeVar("T")	Generic parameter
Protocol	Structural typing
TypedDict	Dict with fixed keys
Literal["a", "b"]	Specific values only
Final	Cannot be reassigned

Type Checker Commands

# mypy
mypy src/ --strict

# pyright
pyright src/

# In pyproject.toml
[tool.mypy]
strict = true
python_version = "3.11"

Additional Resources

• ./references/generics-advanced.md - TypeVar, ParamSpec, TypeVarTuple
• ./references/protocols-patterns.md - Structural typing, runtime protocols
• ./references/type-narrowing.md - Guards, isinstance, assert
• ./references/mypy-config.md - mypy/pyright configuration
• ./references/runtime-validation.md - Pydantic v2, typeguard, beartype
• ./references/overloads.md - @overload decorator patterns

Scripts

• ./scripts/check-types.sh - Run type checkers with common options

Assets

• ./assets/pyproject-typing.toml - Recommended mypy/pyright config

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See Also

This is a foundation skill with no prerequisites.

Related Skills:

• python-pytest-patterns - Type-safe fixtures and mocking

Build on this skill:

• python-async-patterns - Async type annotations
• python-fastapi-patterns - Pydantic models and validation
• python-database-patterns - SQLAlchemy type annotations